Poultry farming for meat production has significantly increased in the last few decades. For example, the per capita consumption of chicken in the United states was estimated to be over 74 pounds in 2004, which represents a 200% increase in les than 20 years, according to the U.S. Department of Agriculture. As a result of this increase in production, fecal matter has become a significant byproduct of the poultry industry. Fecal matter is often used as fertilizer in the form of raw or composted manure. A potential risk arising from the disposal of poultry waste is the spread of enteric pathogens, such as Escherichia coli O157:H7, Salmonella spp., and Campylobacter spp. These pathogens can reach watersheds after rainfall, and thereby increase risks associated with recreational use of waterways. Furthermore, environmental concerns also include high nutrient loads, such as nitrate and phosphate, from runoff to streams, ponds, and ground water. Methods that can specifically detect poultry fecal pollution are therefore needed to assist in the development and evaluation of adequate management practices targeting pollution control.
Current regulatory methods used to assess microbial water quality rely on measuring the levels of culturable fecal indicator bacteria such as Enterococci and other fecal coliforms. However, the plate culture approach cannot discriminate among different among specific bacterial strains or animal sources of fecal contamination.
A limited number of studies have reported on the use of genotypic methods to identify the presence of poultry fecal contamination in surface waters. Ribotyping and rep-PCR DNA fingerprint techniques targeting E. coli isolates have been applied to discriminate among different animal fecal sources, including chicken and human fecal sources. However, the successful application of these genotypic methods depends on the development of large fingerprint databases of indicator bacterial isolates, primarily E. coli. Moreover, the use of E. coli for fecal source identification has been recently criticized in light of the abundance of secondary habitat populations that are capable of adapting to conditions outside of the animal gut and, as a result, contribute to the levels of fecal indicator bacteria in water.
Recently, Field and coworkers used library-independent methods based on ribosomal 16S rRNA gene (i.e., 16S sDNA) sequences of Bacteroides-like bacteria to discriminate between human and ruminant feces. These Bacteroides markers have been used to identify non-point sources of fecal pollution in coastal in inland waters. Analyses of bacterial rDNA sequences from chicken fecal DNA extracts suggests that chicken cecum and ileum are inhabited by a diverse bacterial community. Although the chicken fecal communities are different from cattle and human fecal microbial communities, thus far no studies have demonstrated the value of 16S rDNA sequences to design host-specific genetic markers. Moreover, to date, there are no non-16S rDNA library-independent assays that can determine the presence of chicken fecal pollution in watersheds.
Functional genes involved in host-microbial interactions may represent a good pool of targets for host-specific assays. Some of these functional genes are hypothesized to be microbial surface proteins, while others may be associated with cellular processes and metabolism. However, a limited number of studies have used genes involved in host-microbial interactions as potential fecal community markers. This is probably due to the small number of microbial genes known to be involved in host-microbial interactions and the limited sequence information for these genes.
There is a demand for accurate microbial source tracking (MST), because of language in the U.S. Clean Water Act regarding total maximum daily loads (TMDLs) and protection of supplies of drinking water. Current PCR-based MST approaches focus on various specific known DNA sequences, mostly targeting 16S rRNA (rDNA) genes, once thought to be source specific. However, validation studies are constantly uncovering exceptions and limitations with existing MST technologies. A significant part of the problem with existing 16S rDNA-based MST methods stemmed from the inability to target microorganism DNA sequences encoding for proteins directly involved in host-microbe interactions, which are expected to contain high levels of genetic variation related to survival within different animal hosts.
Many specific approaches have previously attempted to determine sources of fecal contamination in the environment. One of the most widely used techniques is a PCR-based method that identifies ruminant fecal pollution by targeting bacterial 16S rDNA sequences from Bacteroides (Bernard and Field, AEM 66:4571-4574, 2000). The present inventors have conducted ongoing validation studies of this method, and have discovered that previously described proposed ruminant specific markers can amplify rDNA from non-ruminant fecal samples collected from geographic regions outside the original watersheds sampled. By definition, these previously described PCR target regions identify cow, deer, elk, goat, sheep, and other ruminants and pseudo-ruminants. This approach is therefore less useful in watersheds impacted by more than one ruminant animal source.
While advances in DNA sequencing and computational biology allow scientists to compare entire microbial genomes and discern microorganism-specific genetic information, sequencing of multiple closely related bacterial genomes so far remains prohibitively expensive and impractical for all but a very small number of laboratories. The entire genome content of more than 238 bacterial species have so far been defined through whole genome sequencing of representative type strains, and the number of genome sequences continues to increase. While significant differences in the genome content of different species are well-established, comparisons between genomes of closely related bacteria are equally important. These comparisons can provide species and strain-specific genetic information, define metabolic pathways and virulence factors, and provide insights into capacities for host-interactions, cell-to-cell signaling, stress response, and other essential microbial cellular functions.
Current DNA-based technologies potentially capable of identifying source, species, and strain-specific genetic markers include Suppressive Subtractive Hybridization (SSH) (Diatchenko et al., PNAS 93:6025-6030, 1996). This technique uses intentionally biased PCR amplification of nucleic acid pools to enrich for unique segments of restricted DNA relative to non-target DNA. SSH has been successfully applied in several pair-wise comparative genome studies (e.g., Nguyen et al., 2004, AEM 71 2564-2575), but only on one “metagenomic” or total microbial community DNA study (Galbraith et al., 2004; Environmental Microbiology: 928-937). SSH is a negative selection process that relies on unequal PCR amplification to amplify all dissimilar sequences from two nucleic acid pools. This is achieved by adding different self-complementary flanking regions to each of two fragment pools, and inhibition of amplification of only those duplexes that re-anneal relative to new heteroduplexes that form following denaturation and reassociation of the mixture.
One of the limitations of currently available microbial source tracking (MST) methods arises from the inability of previously described techniques to target microorganism DNA sequences potentially encoding proteins directly involved in host-microbe interactions. These regions, unlike rDNA operons, are expected to retain high levels of genetic variation in microbes found in association with different animal hosts.